Alireza Ghazavi Khorasgani


Postgraduate Research Student in Information and Communication Systems

About

My research project

My qualifications

2020
Bachelor of Science (BSc) in Electrical Engineering (Communication)
Isfahan University of Technology
2022
Master of Science (M.Sc.) in Electrical Engineering (Communication Systems), with Distinction; recipient of the Merit Student Award
Isfahan University of Technology

Affiliations and memberships

Graduate Student Member
IEEE Communications Society
IEEE Young Professionals
IEEE, Elsevier and UKRI
Reviewer

Research

Research interests

Teaching

Publications

Alireza Qazavi Khorasgani, Foroogh S Tabataba, Mehdi Naderi Soorki, Mohammad Sadegh Fazel, Alireza Ghazavi Khorasgani Dynamic Reflections: Optimizing Energy Efficiency in Multi-IRS Empowered Green Networks, In: arXiv (Cornell University)

Intelligent Reflecting Surface (IRS) technology is revolutionizing wireless communications by shifting from channel adaptation to a responsive wireless environment. This paper introduces a multi-IRS assisted millimeter wave (mm-wave) system, allowing intelligent on/off control of individual IRS elements. Our objective is to optimize energy efficiency under Quality of Service (QoS) constraints. We propose an algorithm where the Access Point (AP) adjusts transmit beamforming, and IRS elements control phaseshifts and on/off status until convergence. Utilizing a fractional programming (FP) approach for AP beamforming and Simulated Annealing (SA) for IRS subproblems, we achieve a suboptimum optimal solution. A modified nested FP approach addresses the beamforming subproblem. Performance analysis in a practical scenario reveals a significant up to 132.16\% improvement in energy efficiency compared to scenarios with randomly selected IRS on/off status. This highlights the efficacy of our algorithm in enhancing mm-wave communication systems' overall efficiency.

Alireza Ghazavi Khorasgani, Mahtab Mirmohseni, Ahmed Elzanaty Optical ISAC: Fundamental Performance Limits and Transceiver Design

This paper characterizes the optimal capacity-distortion (C-D) tradeoff in an optical point-to-point (P2P) system with single-input single-output for communication and single-input multiple-output for sensing (SISO-COM and SIMO-SEN) within an integrated sensing and communication (ISAC) framework. We consider the optimal rate-distortion (R-D) region and explore several inner (IB) and outer (OB) bounds. We introduce practical, asymptotically optimal maximum a posteriori (MAP) and maximum likelihood estimators (MLE) for target distance, addressing nonlinear measurement-to-state relationships and non-conjugate priors. As the number of sensing antennas increases, these estimators converge to the Bayesian Cram'er-Rao bound (BCRB). We also establish that the achievable rate-CRB (AR-CRB) serves as an OB for the optimal C-D region, valid for both unbiased estimators and asymptotically large numbers of receive antennas. To clarify that the input distribution determines the tradeoff across the Pareto boundary of the C-D region, we propose two algorithms: i) an iterative Blahut-Arimoto algorithm (BAA)-type method, and ii) a memory-efficient closed-form (CF) approach. The CF approach includes a CF optimal distribution for high optical signal-to-noise ratio (O-SNR) conditions. Additionally, we adapt and refine the Deterministic-Random Tradeoff (DRT) to this optical ISAC context.

Alireza Ghazavi Khorasgani, Foroogh S. Tabataba, Mohammad Sadegh Fazel, Mehdi Naderi Soorki Dynamic Energy Efficient Resource Allocation in Multi-User Multi-IRS mmWave 6G Networks, In: Physical communication

This study introduces a novel approach for energy-efficient resource allocation in millimeter-wave networks, assisted by multiple intelligent reflecting surfaces (IRS). The proposed framework optimizes the dynamic ON/OFF control and phase shifts of Intelligent Reflecting Surfaces (IRS) elements, along with beamforming (BF) at Access Points (AP), under practical constraints. Unlike existing approaches, our model enhances Energy Efficiency (EE) by optimizing a fixed number of ON IRS elements. We propose innovative algorithms, including modified nested fractional programming (NFP) for BF and Simulated Annealing (SA)-type algorithm for phase shift and element selection. Our framework satisfies quality-of-service (QoS) requirements while addressing practical IRS deployment limitations. Results show a 132.17% improvement in EE under realistic scenarios, highlighting the potential of our method as a key strategy for future 6G networks.

Alireza Ghazavi Khorasgani, Foroogh S. Tabataba, Mehdi Naderi Soorki (2022)Joint User Association and UAV Location Optimization for Two-Tired Visible Light Communication Networks, In: 2022 30th International Conference on Electrical Engineering (ICEE)pp. 755-761 IEEE

In this paper, an unmanned aerial vehicle (UAVs)-assisted visible light communication (VLC) has been considered which has two tiers: UAV-to-centroid and device-to-device (D2D). In the UAV-to-centroid tier, each UAV can simultaneously provide communications and illumination for the centroids of the ground users over VLC links. In the D2D tier, the centroids retransmit received data from UAV over D2D links to the cluster members. For network, the optimization problem of joint user association and deployment location of UAVs is formulated so as to maximize the received data, satisfy illumination constraints, and also the user cluster size. An iterative algorithm is first proposed to transform the optimization problem into a series of two interdependent sub problems. Following the smallest enclosing disk theorem, a random incremental construction method is designed to find the optimal UAV locations. Then, inspired by unsupervised learning method, a clustering algorithm to find a suboptimal user association is proposed. Our simulation results show that the proposed scheme on average guarantees the users brightness 0.3 microwatt more than their threshold requirements. Moreover, the received bitrate plus number of D2D connected users under our proposed method is 55.0% more than the scenario in which we do not optimize UAV location.